Predicting Stock Movement using Sentiment Analysis of Twitter Feed

被引：0

作者：

Chakraborty, Pranjal ^{[1
]}

Pria, Ummay Sani ^{[1
]}

Rony, Md Rashad Al Hasan ^{[1
]}

Majumdar, Mahbub Alam ^{[1
]}

机构：

[1] BRAC Univ, Dept Comp Sci & Engn, 66 Mohakhali, Dhaka, Bangladesh

来源：

2017 6TH INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS AND VISION & 2017 7TH INTERNATIONAL SYMPOSIUM IN COMPUTATIONAL MEDICAL AND HEALTH TECHNOLOGY (ICIEV-ISCMHT) | 2017年

关键词：

Tweets; Sentiment analysis; Decision tree; Opinion mining; Machine Learning; Random forest; Boosted tree; Support Vector Machine (SVM);

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Collecting data from social networking sites is a popular way of opinion mining. These opinions show the sentimental state of a large number of people. In this paper, we have shown how much we can predict stock movement from twitter's tweets sentiment analysis. Our work is done on one year's (2016) data of tweets that contained 'stock market', 'stocktwits', 'AAPL' keywords. `AAPL' related tweets were used to see if these tweets can predict the company's stock indices whereas 'stock market', 'stocktwits' related tweets for predicting the stock market movement of US. Since we are predicting the stock values, we used Boosted Regression Tree model for this purpose.

引用

页数：6

共 50 条

[31] SENTIMENT ANALYSIS ON TWITTER USING STREAMING API
Trupthi, M.
Pabboju, Suresh
Narasimha, G.
2017 7TH IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC), 2017, : 915 - 919
[32] Sentiment Analysis on Twitter using Ordinal Regression
Ahmed, Moin
Goel, Mohit
Kumar, Raju
Bhat, Aruna
2021 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021, 2021,
[33] Twitter Sentiment Analysis Using Enhanced BERT
Mann, Suman
Arora, Jyoti
Bhatia, Mudita
Sharma, Ritika
Taragi, Rewangi
INTELLIGENT SYSTEMS AND APPLICATIONS, ICISA 2022, 2023, 959 : 263 - 271
[34] Predicting the Stock Market Behavior Using Historic Data Analysis and News Sentiment Analysis in R
Jishag, A. C.
Athira, A. P.
Shailaja, Muchintala
Thara, S.
FIRST INTERNATIONAL CONFERENCE ON SUSTAINABLE TECHNOLOGIES FOR COMPUTATIONAL INTELLIGENCE, 2020, 1045 : 717 - 728
[35] Sentiment analysis on twitter
Department of Computer Engineering, Delhi Technological University Delhi, India
Int. J. Comput. Sci. Issues, 2012, 4 4-3 (372-378):
[36] Sentiment Analysis on Twitter
Meral, Meric
Diri, Banu
2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2014, : 690 - 693
[37] Twitter Sentiment Analysis
Sarlan, Aliza
Nadam, Chayanit
Basri, Shuib
PROCEEDINGS OF THE 2014 6TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND MULTIMEDIA (ICIM), 2014, : 212 - 216
[38] Forecasting Stock Market Movement Direction Using Sentiment Analysis and Support Vector Machine
Ren, Rui
Wu, Desheng Dash
Liu, Tianxiang
IEEE SYSTEMS JOURNAL, 2019, 13 (01): : 760 - 770
[39] Sentiment analysis in Twitter
Martinez-Camara, Eugenio
Teresa Martin-Valdivia, M.
Alfonso Urena-Lopez, L.
Montejo-Raez, Arturo
NATURAL LANGUAGE ENGINEERING, 2014, 20 (01) : 1 - 28
[40] Sentiment analysis with Twitter
Akgul, Eyup Sercan
Ertano, Caner
Diri, Banu
PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI, 2016, 22 (02): : 106 - 110

← 1 2 3 4 5 →